Utah’s announcement that their program will go D1 in 2019 is huge news. Lots of people have written about how this changes (or doesn’t change) the landscape of lacrosse in the United States. My focus is going to be a bit less grandiose: how does the current geographic distribution of talent help or hurt Utah’s program building efforts?
This is the first in a series of posts on this topic of geography and talent distribution:
- Stocking Utah’s Pond
- Lacrosse Talent is Heading West (and South)
- Lacrosse Talent is Heading West (Canadian Edition)
- The best D1 lacrosse players choose schools farther from their hometowns
Before we dive in, I want to take a minute to appropriately congratulate the players that made Utah into the program it is today. As I was writing this article, it occurred to me that it could be misconstrued as describing Utah as a newborn program. That is not true. The players that have sacrificed to play lacrosse at Utah are 95% of why we are even talking about this. So when we say “stocking Utah’s pond”, it just means that the pond has gotten a lot bigger and as a result, will need more fish.
Anyway, I wrote about the geographic distribution of D1 players earlier this past season. The idea was to look at the state that each player is from to see if we can ferret out any trends in the recruiting world. Are certain states sending more players to D1 programs? Are teams going outside the traditional hotbeds more to stock their rosters?
Utahns in D1?
The most important number to consider here is that Utah has experienced infinite growth in the percentage of its players making up D1 rosters. Infinity!
But in all seriousness, they have seen growth in the years we’ve been tracking this. In 2014, 0.0% if the D1 men’s players were from Utah. By 2017, it was 0.3%. The problem is that this translates into only 9 players. And since 4 players were at Rutgers, only 6 teams that have landed a Utahn since we started looking at this data.
Spoiler alert: Utah is not going to be able to stay in state and field a competitive team. Even if we expand this to Utah and surrounding states, we still only have 20 players in total. (Arizona – who knew?)
The previous analyses highlighted a few trends relevant to this discussion.
First, the state of Maryland has been losing market-share in the top tier of D1. Teams have sourced fewer and fewer of their players from Maryland in each year that we have data for. But the trend is fairly limited to the top-tier teams (check the bottom of this article for “top-tier” definition). Non-top-tier teams have been getting as many players from Maryland as ever.
Second, as opposed to the Maryland point, it’s not clear that the traditional hotbeds have been losing market-share all that much. And if they are, it’s slowly. There really was no consistent pattern regardless of how you defined “traditional hot-bed”.
Third, California was the big gainer across all years of our analysis. They experienced the largest increase of any state. In 2014, they accounted for 3.3% of all D1 players. In 2017, that number was up to 4.6%. Washington also came in as one of the states with a higher than average growth rate.
What does it mean for Utah?
There are a few different ways that you could view this from the perspective of the Utes.
On the one hand, there are more players from the west coast in D1 than ever before. The goal of each team is to create the best roster they can given their resources, so the fact that they are increasingly dipping into the western half of the country means that either 1) it’s more cost effective to recruit these players or 2) they are finding better players out west. Since every program in D1 is at least a flight away from California and the state hasn’t moved recently, I’m pretty sure it’s not scenario 1.
On the other hand, rival teams have already started to raid the stocks of top players out west. More players means that more teams are sending more coaches out to the region, so Utah is behind the curve to some extent.
All things being equal, this analysis has to be viewed as a positive for Utah. More quality players are coming from out west, and they have a geographic advantage. It’s cheaper for them to send personnel to recruit this part of the country. Therefore they can put more resources into getting the best players.
Will Utah be more desirable than east coast schools?
You’ll notice that I didn’t include proximity to home as an advantage for the Utes. It’s very plausible that a graduating high school star would want to play close to home. But I haven’t seen any sort of evidence that this is actually true. Until that is available, I don’t think it’s fair to assume that players will want to go to Utah.
And anyway, if you live in Southern California, is Utah really all that close? It’s not like you are jetting home to do laundry. We east-coasters tend to understate just how far places are from each other out west.
But I think that this question will be key to understanding the trajectory of the program over the next decade.
How can we find out?
So what would that analysis look like? If we wanted to understand how much of a draw “close to home” really is, how would we do it? I think there would be a few considerations.
First, you’d need to have some way to hold player quality and program quality constant. No offense to Hobart, but I’d imagine that a player with offers from them and Syracuse is probably more likely to go to Syracuse. (Please tell me if I’m wrong about that.) The distance is roughly the same, so you need to be able to account for whatever causes that disparity.
You’d also need to find a way to control for player quality. It may be that star players are more likely to venture far from home. Again, I have no evidence of this, but it is plausible, and if the effect exists, it needs to be controlled for. Perhaps Utah will have an advantage among the second-tier of recruits. But if that is the case, then the advantage of having more blue-chippers coming from out west will be muted.
A first check is to look at the distribution of distances of a player from each school and see whether the top programs have larger or smaller distances on average. If you break that down by region or school, do you see any other discrepancies? Understanding this is key to adjusting for school strength. We will need a similar process for player ratings.
(Additional challenge here is that recruiting rankings are not comprehensive; you’d rather look at how highly regarded a player was coming from high school. We will probably have to look at after-the-fact metrics to understand how good they turned out to be.)
Regression shows the way
But once you’ve done that, I think it would be fairly simple to do a quick calculation that identifies the effect of distance on school choice. Once you’ve accounted for program strength and player strength, it’s just a question of measuring the (presumably) increasing likelihood of a closer school being selected. Since we won’t have data on individual offers, we’ll have to use all players and all schools.
I’ll introduce the concept of selection-points here. Each school receives selection points based on the quality of the players they’ve attracted and the attractiveness of the program. Maryland gets (I don’t know, call it) 500 selection points because Matt Rambo picked them. Maybe that is 450 points because he was awesome and 50 points because Maryland is a blue-chip program (and so should be getting more top players). A player half as good as Rambo might give his school 400 selection points by picking them. Maybe that works out to 125 points because he is half as good as Rambo and 275 points because the program is Cleveland State. (Note: we will determine the ratio of talent points versus program points when the model is calibrated.)
If distance makes no difference, then we would expect that you can explain 100% of a team’s selection points by a simple two factor equation using player strength and program attractiveness. That’ll be our null hypothesis. However, if we see that a school’s collective distance from the D1 player stock helps explain more of our selection points plot, then we should be able to say that distance matters.
This of course, assumes that program strength, player quality, and distance are the only three factors in a decision. Roster construction matters of course. Coaching changes matter of course.
Think of it this way: if 50% of a program’s selection points can be explained with program attractiveness and player strength, but adding distance gets us to 90%, then we can be pretty sure that it matters. If we start at 85% with those two factors, and distance gets us to 90%, then I’d say we would say it really doesn’t matter much.
Why does this matter?
At its best, an analysis like this should highlight some potential strategies that the Utah coaching staff could pursue as they gear up for D1. Should they be highlighting their proximity to the West Coast when recruiting in those households? Or do they spend more time trying to highlight the attractiveness of the program to potential recruits? This has real operational implications.
Budgets are tight and while many have written about the financial heft of Utah’s athletic department, I highly doubt Chris Hill gave the lacrosse team a blank check. That means that the program will compare every recruiting trip against other initiatives that could grow the program. Do you spend $5,000 on a recruiting trip or spend $5,000 on marketing materials to draw in more fans?
I would argue that if our analysis shows that distance matters, then they should be on flights to every top recruit on the west coast. And they should give every parent a map with the route between home and Salt Lake City featured prominently. In other words, if distance matters, and you have the geographic advantage over everyone, then press it.
But, if this shows that program attractiveness matters more than distance, then they should take a more “if you build it, they will come” attitude. That means paying for facilities, speakers, programs for the athletes, and fan-base efforts.
At the end of the day, building a program is really hard. If Utah is able to allocate resources in the most efficient way, the chances of success increase. I think we are comfortable saying that.
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